Principles, Techniques, and Applications of T2*-based MR Imaging and Its Special Applications
Bibliographic record
Abstract
T2* relaxation refers to decay of transverse magnetization caused by a combination of spin-spin relaxation and magnetic field inhomogeneity. T2* relaxation is seen only with gradient-echo (GRE) imaging because transverse relaxation caused by magnetic field inhomogeneities is eliminated by the 180 degrees pulse at spin-echo imaging. T2* relaxation is one of the main determinants of image contrast with GRE sequences and forms the basis for many magnetic resonance (MR) applications, such as susceptibility-weighted (SW) imaging, perfusion MR imaging, and functional MR imaging. GRE sequences can be made predominantly T2* weighted by using a low flip angle, long echo time, and long repetition time. GRE sequences with T2*-based contrast are used to depict hemorrhage, calcification, and iron deposition in various tissues and lesions. SW imaging uses phase information in addition to T2*-based contrast to exploit the magnetic susceptibility differences of the blood and of iron and calcification in various tissues. Perfusion MR imaging exploits the signal intensity decrease that occurs with the passage of a high concentration of gadopentetate dimeglumine through the microvasculature. Change in oxygen saturation during specific tasks changes the local T2*, which leads to the blood oxygen level-dependent effect seen at functional MR imaging. The basics of T2* relaxation, T2*-weighted sequences, and their clinical applications are presented, followed by the principles, techniques, and clinical uses of four T2*-based applications, including SW imaging, perfusion MR imaging, functional MR imaging, and iron overload imaging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".